Granular Computing-driven SAM: From Coarse-to-Fine Guidance for Prompt-Free Segmentation

• URL: http://arxiv.org/abs/2511.19062v1
• Date: Mon, 24 Nov 2025 12:55:02 GMT
• Title: Granular Computing-driven SAM: From Coarse-to-Fine Guidance for Prompt-Free Segmentation
• Authors: Qiyang Yu, Yu Fang, Tianrui Li, Xuemei Cao, Yan Chen, Jianghao Li, Fan Min, Yi Zhang,
• Abstract summary: We introduce Granular Computing-driven SAM (Grc-SAM), a coarse-to-fine framework motivated by Granular Computing.<n>First, the coarse stage adaptively extracts high-response regions from features to achieve precise foreground localization.<n>Second, the fine stage applies finer patch partitioning with sparse local swin-style attention to enhance detail modeling.<n>Third, refined masks are encoded as latent prompt embeddings for the SAM decoder, replacing handcrafted prompts with an automated reasoning process.
• Score: 17.190865623538212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt-free image segmentation aims to generate accurate masks without manual guidance. Typical pre-trained models, notably Segmentation Anything Model (SAM), generate prompts directly at a single granularity level. However, this approach has two limitations: (1) Localizability, lacking mechanisms for autonomous region localization; (2) Scalability, limited fine-grained modeling at high resolution. To address these challenges, we introduce Granular Computing-driven SAM (Grc-SAM), a coarse-to-fine framework motivated by Granular Computing (GrC). First, the coarse stage adaptively extracts high-response regions from features to achieve precise foreground localization and reduce reliance on external prompts. Second, the fine stage applies finer patch partitioning with sparse local swin-style attention to enhance detail modeling and enable high-resolution segmentation. Third, refined masks are encoded as latent prompt embeddings for the SAM decoder, replacing handcrafted prompts with an automated reasoning process. By integrating multi-granularity attention, Grc-SAM bridges granular computing with vision transformers. Extensive experimental results demonstrate Grc-SAM outperforms baseline methods in both accuracy and scalability. It offers a unique granular computational perspective for prompt-free segmentation.

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